Speaker Characterization Through Speech Analysis

Degani, Yoav; Zamir, Yishai

doi:10.1121/1.4770449

Cited by 2 publications

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“…The analysis was validated versus various personality tools, such as Big 5 [ 23 ], Holland [ 24 ], Hogan [ 25 ], WPI [ 26 ], and linked speech patterns (clusters of the raw speech parameters) with the personality scales as measured by these tools. The analysis is based on several granted patents regarding measurement of emotional state [ 27 ] and behavioral tendencies [ 28 ] through speech analysis. This proprietary speech anal ysis is the basis for commercial products in the fields of enterprise, big data analytics, customer analytics, human resources, and others.…”

Section: Methodsmentioning

confidence: 99%

Development of a Digital Content-Free Speech Analysis Tool for the Measurement of Mental Health and Follow-Up for Mental Disorders: Protocol for a Case-Control Study

Tonn¹,

Degani²,

Hershko³

et al. 2020

JMIR Res Protoc

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Background The prevalence of mental disorders worldwide is very high. The guideline-oriented care of patients depends on early diagnosis and regular and valid evaluation of their treatment to be able to quickly intervene should the patient’s mental health deteriorate. To ensure effective treatment, the level of experience of the physician or therapist is of importance, both in the initial diagnosis and in the treatment of mental illnesses. Nevertheless, experienced physicians and psychotherapists are not available in enough numbers everywhere, especially in rural areas or in less developed countries. Human speech can reveal a speaker’s mental state by altering its noncontent aspects (speech melody, intonations, speech rate, etc). This is noticeable in both the clinic and everyday life by having prior knowledge of the normal speech patterns of the affected person, and with enough time spent listening to the patient. However, this time and experience are often unavailable, leaving unused opportunities to capture linguistic, noncontent information. To improve the care of patients with mental disorders, we have developed a concept for assessing their most important mental parameters through a noncontent analysis of their active speech. Using speech analysis for the assessment and tracking of mental health patients opens up the possibility of remote, automatic, and ongoing evaluation when used with patients’ smartphones, as part of the current trends toward the increasing use of digital and mobile health tools. Objective The primary objective of this study is to evaluate measurements of participants' mental state by comparing the analysis of noncontent speech parameters to the results of several psychological questionnaires (Symptom Checklist-90 [SCL-90], the Patient Health Questionnaire [PHQ], and the Big 5 Test). Methods In this paper, we described a case-controlled study (with a case group and one control group). The participants will be recruited in an outpatient neuropsychiatric treatment center. Inclusion criteria are a neurological or psychiatric diagnosis made by a specialist, no terminal or life-threatening illnesses, and fluent use of the German language. Exclusion criteria include psychosis, dementia, speech or language disorders in neurological diseases, addiction history, a suicide attempt recently or in the last 12 months, or insufficient language skills. The measuring instrument will be the VoiceSense digital voice analysis tool, which enables the analysis of 200 specific speech parameters, and the assessment of findings using psychometric instruments and questionnaires (SCL-90, PHQ, Big 5 Test). Results The study is ongoing as of September 2019, but we have enrolled 254 participants. There have been 161 measurements completed at timepoint 1, and a total of 62 participants have completed every psychological and speech analysis measurement. Conclusions It appears that the tone and modulation of speech are as important, if not more so, than the content, and should not be underestimated. This is particularly evident in the interpretation of the psychological findings thus far acquired. Therefore, the application of a software analysis tool could increase the accuracy of finding assessments and improve patient care. Trial Registration ClinicalTrials.gov NCT03700008; https://clinicaltrials.gov/ct2/show/NCT03700008 International Registered Report Identifier (IRRID) PRR1-10.2196/13852

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Section: Methodsmentioning

confidence: 99%

Development of a Digital Content-Free Speech Analysis Tool for the Measurement of Mental Health and Follow-Up for Mental Disorders: Protocol for a Case-Control Study

Tonn¹,

Degani²,

Hershko³

et al. 2020

JMIR Res Protoc

Self Cite

View full text Add to dashboard Cite

show abstract

“…The analysis was validated versus various personality tools, such as Big 5 [23], Holland [24], Hogan [25], WPI [26], and linked speech patterns (clusters of the raw speech parameters) with the personality scales as measured by these tools. The analysis is based on several granted patents regarding measurement of emotional state [27] and behavioral tendencies [28] through speech analysis. This proprietary speech anal ysis is the basis for commercial products in the fields of enterprise, big data analytics, customer analytics, human resources, and others.…”

Section: Speech Analysismentioning

confidence: 99%

Development of a digital content-free speech analysis for the measurement of mental health and follow-up of mental disorders (Preprint)

Tonn¹,

Degani²,

Hershko³

et al. 2019

Preprint

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UNSTRUCTURED Background Worldwide, the prevalence of mental disorders is very high and the guideline-oriented care of patients depends greatly on an early diagnosis as well as a regular and valid evaluation of the course, to be able to intervene quickly in case of imminent recurrence or deterioration in therapeutic terms. Experienced physicians and psychotherapists are neccessary for diagnostics and treatment but not available in sufficient numbers everywhere, especially in rural areas or in less well-developed countries. The human language is capable of revealing the psychic situation of the speaker by altering the non-contentual aspects (speech melody, intonations, speech rate, etc.). The time and experience to learn the speechpatterns of a patient in healthy and ill moments is often unavailable, leaving the opportunities inherent in capturing linguistic, non-contentual information unused. In order to improve the care of patients with mental disorders just under these aspects, we have developed a concept for assessing the most important mental parameters through a non-contentual analysis of the active speech. Using speech analysis for assessment and tracking of mental health patients, opens also the invaluable possibilities of remote, automatic and ongoing evaluation, when used with patients‘ smartphones, as part of the strong digital and mobile health trends. Methods/Design In this paper, we describe a two-arm, randomized controlled trial. The participants are all recruited in one outpatient neuropsychiatric treatment center. Inclusion criteria are e.g. a psychiatric diagnosis made by a specialist, no terminal or life-threatening illness, fluent use of the German language, exclusion criteria are e.g. psychosis, dementia, speech or language disorders, addiction history or suicide attempt currently or in the last 12 months. The measuring instruments are the "VoiceSense" voice analysis tool, which enables the analysis of 200 specific speech parameters and assessment of the findings through the use of psychometric questionnaires. Discussion The importance of content-free speech patterns should not be overestimated. This is particularly evident in the interpretation of the psychological findings. Applying a software analysis tool can increase the accuracy of finding assignments and improve patient care. Trial Registration This study is registered at „clinicaltrials.gov“, Number was NCT03700008, registration date 09 October 2018, http://www.clinicaltrials.gov.

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Speaker Characterization Through Speech Analysis

Cited by 2 publications

References 0 publications

Development of a Digital Content-Free Speech Analysis Tool for the Measurement of Mental Health and Follow-Up for Mental Disorders: Protocol for a Case-Control Study

Development of a Digital Content-Free Speech Analysis Tool for the Measurement of Mental Health and Follow-Up for Mental Disorders: Protocol for a Case-Control Study

Development of a digital content-free speech analysis for the measurement of mental health and follow-up of mental disorders (Preprint)

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